# Foundational areas. The first part will consist of a brief introduction to each of the four foundational areas of SRL: logical inference, inductive logic programming, probabilistic inference, and statistical learning. Obviously, in the short time available no attempt will be made to comprehensively survey these areas; rather, the focus will be on providing the key concepts and techniques required for the subsequent parts. For example, the logical inference part will focus on the basics of satisfiability testing, and the probabilistic/statistical parts on Markov networks. The duration of this part will be approximately two hours (half hour per subtopic).

# Putting the pieces together. The second part will introduce the key ideas in SRL and survey major approaches, using Markov logic as the unifying framework. It will present state-of-the-art algorithms for statistical relational learning and inference, and give an overview of the Alchemy open-source software. This part will essentially consist of putting together the pieces introduced in the first part. Its duration will be approximately an hour.

# Applications. The third and final part will describe how to efficiently develop state-of-the-art non-i.i.d. applications in various areas, including: hypertext classification, link-based information retrieval, information extraction and integration, natural language processing, social network modeling, computational biology, and ubiquitous computing. This part will also include practical tips on using SRL, Markov logic and Alchemy – the kind of information that is seldom found in research papers, but is key to developing successful applications. The duration of this part will be approximately an hour.

We propose statistical predicate invention as a key problem for statistical relational learning. SPI is the problem of discovering new concepts, properties and relations in structured data, and generalizes hidden variable discovery in statistical models and predicate invention in ILP. We propose an initial model for SPI based on second-order Markov logic, in which predicates as well as arguments can be variables, and the domain of discourse is not fully known in advance. Our approach iteratively refines clusters of symbols based on the clusters of symbols they appear in atoms with (e.g., it clusters relations by the clusters of the ob jects they relate). Since different clusterings are better for predicting different subsets of the atoms, we allow multiple cross-cutting clusterings. We show that this approach outperforms Markov logic structure learning and the recently introduced infinite relational model on a number of relational datasets.